Search results for: artificial pollution.

Authors: Ian A. McCulloh, Madelaine Zinser, Jesse Patsolic, Michael Ramos

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Recommender systems are a common artificial intelligence (AI) application. For any given input, a search system will return a rank-ordered list of similar items. As users review returned items, they must decide when to halt the search and either revise search terms or conclude their requirement is novel with no similar items in the database. We present a statistically designed experiment that investigates the impact of similarity ratings on human judgement to conclude a search item is novel and halt the search. In the study, 450 participants were recruited from Amazon Mechanical Turk to render judgement across 12 decision tasks. We find the inclusion of ratings increases the human perception that items are novel. Percent similarity increases novelty discernment when compared with star-rated similarity or the absence of a rating. Ratings reduce the time to decide and improve decision confidence. This suggests that the inclusion of similarity ratings can aid human decision-makers in knowledge search tasks.

Keywords: Ratings, rankings, crowdsourcing, empirical studies, user studies, similarity measures, human-centered computing, novelty in information retrieval.

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Authors: K.Sedhuraman, S.Himavathi, A.Muthuramalingam

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The performance of sensor-less controlled induction motor drive depends on the accuracy of the estimated speed. Conventional estimation techniques being mathematically complex require more execution time resulting in poor dynamic response. The nonlinear mapping capability and powerful learning algorithms of neural network provides a promising alternative for on-line speed estimation. The on-line speed estimator requires the NN model to be accurate, simpler in design, structurally compact and computationally less complex to ensure faster execution and effective control in real time implementation. This in turn to a large extent depends on the type of Neural Architecture. This paper investigates three types of neural architectures for on-line speed estimation and their performance is compared in terms of accuracy, structural compactness, computational complexity and execution time. The suitable neural architecture for on-line speed estimation is identified and the promising results obtained are presented.

Keywords: Sensorless IM drives, rotor speed estimators, artificial neural network, feed- forward architecture, single neuron cascaded architecture.

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Authors: Abdelrasoul, Khalid Ahmed Elrabie

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The study was conducted on Sahiwal cattle bulls maintained at the Artificial Breeding Complex, NDRI, Karnal, Hayana, India, to determine the effect of exercise on the sexual behavior and semen quality. Fourteen Sahiwal bulls were classified into two groups of seven each. Group-1, bulls were exercised by walking in a bull exerciser once a week one hour before semen collection, whereas bulls in group-2 were exercised daily. Sexual behavior and semen quality traits studied were: Reaction time (RT), Dismounting time (DMT), Total time taken in mounts (TTTM), Flehmen response (FR), Erection Score (ES), Protrusion Score (PS), Intensity of thrust (ITS), Temperament Score (TS), Libido Score (LS), Semen volume, Physical appearance, Mass activity, Initial progressive motility, Non-eosinophilic spermatozoa count (NESC) and post thaw motility percent. Data were analyzed by least squares technique. Group-2 showed significantly (p < 0.01) higher value in RT (sec), DMT (sec), TTTM (sec), ES, PS, ITS, LS, semen volume, semen color density and mass activity.

Keywords: Exercise, Sahiwal bulls, semen quality, sexual behavior.

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Authors: Jandot Aurélia

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In France, in the main media, the concern about nuclear safety and security has not really appeared before the beginning of the 1970s. The gradual changes in its perception are studied here through the arguments given in the main French news magazines, linked with several parameters. As this represents a considerable amount of copies and thus of information, are selected here the main articles as well as the main “mental images” aiming to persuade the readers and which have led the public awareness to evolve. Indeed, in the 1970s, in France, these evolutions were not made in one day. Indeed, over the period, many articles were still in favor of nuclear power plants and promoted the technological advances that were made in this field. They had to be taken into account. But, gradually, grew up arguments and mental images discrediting the perception of nuclear technology. Among these were the environmental impacts of this industry, as the question of pollution progressively appeared. So, between 1970 and 1979, the language has changed, as the perceptible objectives of the communication, allowing to discern the deepest intentions of the editorial staffs of the French news magazines. This is all these changes that are emphasized here, over a period when the safety and security concern linked to the nuclear technology, to there a field for specialists, has become progressively a social issue seemingly open to all.

Keywords: French media discourse, nuclear safety and security, public awareness, persuasion.

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Authors: M. Boudjerda, B. Touaibia, M. K. Mihoubi

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In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: ater management, agricultural demand, dam and reservoir operation, Foum el-Gherza dam, dynamic programming, artificial neural network.

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Authors: Deepak Goswami, Padma Lochan Hazarika, Kandarpa Kumar Sarma

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The customary practice of identifying industrial sickness is a set traditional techniques which rely upon a range of manual monitoring and compilation of financial records. It makes the process tedious, time consuming and often are susceptible to manipulation. Therefore, certain readily available tools are required which can deal with such uncertain situations arising out of industrial sickness. It is more significant for a country like India where the fruits of development are rarely equally distributed. In this paper, we propose an approach based on Artificial Neural Network (ANN) to deal with industrial sickness with specific focus on a few such units taken from a less developed north-east (NE) Indian state like Assam. The proposed system provides decision regarding industrial sickness using eight different parameters which are directly related to the stages of sickness of such units. The mechanism primarily uses certain signals and symptoms of industrial health to decide upon the state of a unit. Specifically, we formulate an ANN based block with data obtained from a few selected units of Assam so that required decisions related to industrial health could be taken. The system thus formulated could become an important part of planning and development. It can also contribute towards computerization of decision support systems related to industrial health and help in better management.

Keywords: Industrial, Health, Classification, ANN, MLP, MSE.

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Authors: Somlak Wannarumon, Apichai Ritvirool, Thana Boonrit

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The paper proposes a new concept in developing collaborative design system. The concept framework involves applying simulation of supply chain management to collaborative design called – 'SCM–Based Design Tool'. The system is developed particularly to support design activities and to integrate all facilities together. The system is aimed to increase design productivity and creativity. Therefore, designers and customers can collaborate by the system since conceptual design. JAG: Jewelry Art Generator based on artificial intelligence techniques is integrated into the system. Moreover, the proposed system can support users as decision tool and data propagation. The system covers since raw material supply until product delivery. Data management and sharing information are visually supported to designers and customers via user interface. The system is developed on Web–assisted product development environment. The prototype system is presented for Thai jewelry industry as a system prototype demonstration, but applicable for other industry.

Keywords: Collaborative design, evolutionary art, jewelry design, supply chain management.

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Authors: Somlak Wannarumon, Apichai Ritvirool, Thana Boonrit

Abstract:

The paper proposes a new concept in developing collaborative design system. The concept framework involves applying simulation of supply chain management to collaborative design called – 'SCM–Based Design Tool'. The system is developed particularly to support design activities and to integrate all facilities together. The system is aimed to increase design productivity and creativity. Therefore, designers and customers can collaborate by the system since conceptual design. JAG: Jewelry Art Generator based on artificial intelligence techniques is integrated into the system. Moreover, the proposed system can support users as decision tool and data propagation. The system covers since raw material supply until product delivery. Data management and sharing information are visually supported to designers and customers via user interface. The system is developed on Web–assisted product development environment. The prototype system is presented for Thai jewelry industry as a system prototype demonstration, but applicable for other industry.

Keywords: Collaborative design, evolutionary art, jewelry design, supply chain management.

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Authors: Haining Wang, Hong Zhang

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Problems exist in the present construction industry in China. Conflicts hinder the development of the whole society, such as contradictions between resource reservation and a huge population, living space needs and low building production efficiency, as well as environment protection and high pollution production pattern. In order to solve the problems and find a solution, research is needed to explore a building system. By investigating the whole architectural process and contrasting analysis of light structures and heavy structures, the paper raised the concepts to cope with the existing challenges, such as design conception based on product and real construction processes, design methods focusing on components, and maximum utilization of the temporary building by optimizing the construction speed and building performance. The project was not only designed in virtual reality, but was also physically constructed in the real world. A series of aluminum light structure housing systems were dictated at last, with the characteristics of high performance, extremely rapid construction speed and also flexible function. It can be used in lots of aspects ranging from a single building in a remote area to a large residential community.

Keywords: Aluminum house, light structure, rapid assembly, repeat construction.

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Authors: Chun-Wei Lin, Yu-Lin Chen

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As global industry developed rapidly, the energy demand also rises simultaneously. In the production process, there’s a lot of energy consumed in the process. Formally, the energy used in generating the heat in the production process. In the total energy consumption, 40% of the heat was used in process heat, mechanical work, chemical energy and electricity. The remaining 50% were released into the environment. It will cause energy waste and environment pollution. There are many ways for recovering the waste heat in factory. Organic Rankine Cycle (ORC) system can produce electricity and reduce energy costs by recovering the waste of low temperature heat in the factory. In addition, ORC is the technology with the highest power generating efficiency in low-temperature heat recycling. However, most of factories executives are still hesitated because of the high implementation cost of the ORC system, even a lot of heat are wasted. Therefore, this study constructs a nonlinear mathematical model of waste heat recovery equipment configuration to maximize profits. A particle swarm optimization algorithm is developed to generate the optimal facility installation plan for the ORC system.

Keywords: Green facility planning, organic rankine cycle, particle swarm optimization, waste heat recovery.

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Authors: C. A. Alves, A. Vicente, M. Evtyugina, C. A. Pio, A. Hoffer, G. Kiss, S. Decesari, R. Hillamo, E.Swietlicki

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The concentrations of aliphatic and polycyclic aromatic hydrocarbons (PAH) were determined in atmospheric aerosol samples collected at a rural site in Hungary (K-puszta, summer 2008), a boreal forest (Hyytiälä,  April 2007) and a polluted rural area in Italy (San Pietro Capofiume, Po Valley, April 2008). A clear distinction between “clean" and “polluted" periods was observed. Concentrations obtained for Hyytiälä are significantly lower than those for the other two sites. Source reconciliation was performed using diagnostic parameters, such as the carbon preference index and ratios between PAH. The presence of an unresolved complex mixture of hydrocarbons, especially for the Finnish and Italian samples, is indicative of petrogenic inputs. In K-puszta, the aliphatic hydrocarbons are dominated by leaf wax n-alkanes. The long range transport of anthropogenic pollution contributed to the Finnish aerosol. Industrial activities and vehicular emissions represent major sources in San Pietro Capofiume. PAH in K-puszta consist of both pyrogenic and petrogenic compounds.

Keywords: Particulate matter, n-alkanes, PAH, BaPE, ruralsites, source reconciliation.

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Authors: Hossein Lotfizadeh, André McDonald, Amit Kumar

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Renewable energy resources, which can supplement space and water heating for residential buildings, can have a noticeable impact on natural gas consumption and air pollution. This study considers a technical analysis of a combined solar water heating system with evacuated tube solar collectors for different solar coverage, ranging from 20% to 100% of the total roof area of a typical residential building located in Edmonton, Alberta, Canada. The alternative heating systems were conventional (non-condensing) and condensing tankless water heaters and condensing boilers that were coupled to solar water heating systems. The performance of the alternative heating systems was compared to a traditional heating system, consisting of a conventional boiler, applied to houses of various gross floor areas. A comparison among the annual natural gas consumption, carbon dioxide (CO₂) mitigation, and emissions for the various house sizes indicated that the combined solar heating system can reduce the natural gas consumption and CO₂ emissions, and increase CO₂ mitigation for all the systems that were studied. The results suggest that solar water heating systems are potentially beneficial for residential heating system applications in terms of energy savings and CO₂ mitigation.

Keywords: CO2 emissions, CO2 mitigation, natural gas consumption, solar water heating system, tankless water heater.

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Authors: Rabia Korkmaz Tan, Şebnem Bora

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The major problem encountered when modeling complex systems with agent-based modeling and simulation techniques is the existence of large parameter spaces. A complex system model cannot be expected to reflect the whole of the real system, but by specifying the most appropriate parameters, the actual system can be represented by the model under certain conditions. When the studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in agent based simulations, and these studies have focused on tuning parameters of a single model. In this study, an approach of parameter tuning is proposed by using metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colonies (ABC), Firefly (FA) algorithms. With this hybrid structured study, the parameter tuning problems of the models in the different fields were solved. The new approach offered was tested in two different models, and its achievements in different problems were compared. The simulations and the results reveal that this proposed study is better than the existing parameter tuning studies.

Keywords: Parameter tuning, agent based modeling and simulation, metaheuristic algorithms, complex systems.

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Authors: Armando Cartenì, Ilaria Henke

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Recently, the Italian government has approved a new law for public contracts and has been laying the groundwork for restarting a planning phase. The government has adopted the indications given by the European Commission regarding the estimation of the external costs within the Cost-Benefit Analysis, and has been approved the ‘Guidelines for assessment of Investment Projects’. In compliance with the new Italian law, the aim of this research was to perform a feasibility study applying quantitative methods regarding the revamping of an Italian tourist railway line. A Cost-Benefit Analysis was performed starting from the quantification of the passengers’ demand potentially interested in using the revamped rail services. The benefits due to the external costs reduction were also estimated (quantified) in terms of variations (with respect to the not project scenario): climate change, air pollution, noises, congestion, and accidents. Estimations results have been proposed in terms of the Measure of Effectiveness underlying a positive Net Present Value equal to about 27 million of Euros, an Internal Rate of Return much greater the discount rate, a benefit/cost ratio equal to 2 and a PayBack Period of 15 years.

Keywords: Cost-benefit analysis, evaluation analysis, demand management, external cost, transport planning, quality.

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Authors: Vishal Awasthi, Atul Kumar Agnihotri

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Face is a non-intrusive strong biometrics for identification of original and dummy facial by different artificial means. Face recognition is extremely important in the contexts of computer vision, psychology, surveillance, pattern recognition, neural network, content based video processing. The availability of a widespread face database is crucial to test the performance of these face recognition algorithms. The openly available face databases include face images with a wide range of poses, illumination, gestures and face occlusions but there is no dummy face database accessible in public domain. This paper presents a face detection algorithm based on the image segmentation in terms of distance from a fixed point and template matching methods. This proposed work is having the most appropriate number of nodal points resulting in most appropriate outcomes in terms of face recognition and detection. The time taken to identify and extract distinctive facial features is improved in the range of 90 to 110 sec. with the increment of efficiency by 3%.

Keywords: Face recognition, Principal Component Analysis, PCA, Linear Discriminant Analysis, LDA, Improved Support Vector Machine, iSVM, elastic bunch mapping technique.

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Authors: Ahmad K. Jassim, Raheem Kh. Al-Subar

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Friction stir welding is a modern and an environmentally friendly solid state joining process used to joint relatively lighter family of materials. Recently, friction stir spot welding has been used instead of resistance spot welding which has received considerable attention from the automotive industry. It is environmentally friendly process that eliminated heat and pollution. In this research, friction stir spot welding has been used to study the possibility to weld AA1100 aluminum alloy sheet with 3 mm thickness by overlapping the edges of sheet as lap joint. The process was done using a drilling machine instead of milling machine. Different tool rotational speeds of 760, 1065, 1445, and 2000 RPM have been applied with manual and automatic compression to study their effect on the quality of welded joints. Heat generation, pressure applied, and depth of tool penetration have been measured during the welding process. The result shows that there is a possibility to weld AA1100 sheets; however, there is some surface defect that happened due to insufficient condition of welding. Moreover, the relationship between rotational speed, pressure, heat generation and tool depth penetration was created.

Keywords: Friction, spot, stir, environmental, sustainable, AA1100 aluminum alloy.

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Authors: Divya Agarwal, Pushpendra S. Bharti

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Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc.

Keywords: Autonomous mobile robots, obstacle avoidance, path planning, and processing time.

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Authors: Nguyen Minh Tuan, Yen Chung-Ruey

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By hand pollination was conducted to evaluated different pollen sources and their affects on fruit set and quality of wax apple. The following parameters were recorded: fruit set, seed set, fruit characteristics. Results showed that fruit set percentage with seed were significantly high in ‘Long Red B’ when ‘Black’, ‘Thyto’ were used as pollen parents. Pollen of ‘Black’, ‘Thyto’ resulted in high fruit weight, fruit diameter, fruit length, bigger flesh thickness, better total soluble solids as compared with other pollens. The observation of pollen-growth in vitro revealed that pollen germination at 15% sucrose concentration are required for optimum pollen germination with the high pollen germination were found in ‘Black’, ‘Thyto’. From the result, we concluded that ‘Black’, ‘Thyto’ were proved to be good pollinizers in ‘Long Red B’. Therefore, artificial cross-pollination using ‘Black’, ‘Thyto’ as pollinizers were strongly recommended for ‘Long Red B’ cultivar in wax apple orchard.

Keywords: Wax apple, pollination, pollen source, in vitro, fruit quality.

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Authors: Jingming Wu, Ruikang Hu, Junqi Yue, Zhaoguang Yang, Lifeng Zhang

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Fecal sterol has been proposed as a chemical indicator of human fecal pollution even when fecal coliform populations have diminished due to water chlorination or toxic effects of industrial effluents. This paper describes an improved derivatization procedure for simultaneous determination of four fecal sterols including coprostanol, epicholestanol, cholesterol and cholestanol using gas chromatography-mass spectrometry (GC-MS), via optimization study on silylation procedures using N-O-bis (trimethylsilyl)-trifluoroacetamide (BSTFA), and N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (MTBSTFA), which lead to the formation of trimethylsilyl (TMS) and tert-butyldimethylsilyl (TBS) derivatives, respectively. Two derivatization processes of injection-port derivatization and water bath derivatization (60 oC, 1h) were inspected and compared. Furthermore, the methylation procedure at 25 oC for 2h with trimethylsilydiazomethane (TMSD) for fecal sterols analysis was also studied. It was found that most of TMS derivatives demonstrated the highest sensitivities, followed by methylated derivatives. For BSTFA or MTBSTFA derivatization processes, the simple injection-port derivatization process could achieve the same efficiency as that in the tedious water bath derivatization procedure.

Keywords: Fecal Sterols, Methylation, Silylation, BSTFA, MTBSTFA, TMSD, GC-MS.

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Authors: Suttipong Phosuksirikul, Rawichar Chaipojjana, Arunsri Leejeerajumnean

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The aim of research was to define the relations between volatile compounds, some parameters (pH, titratable acidity (TA), total soluble solid (TSS), lactic acid bacteria count) and consumer preference of commercial fermented milks. These relations tend to be used for controlling and developing new fermented milk product. Three leading commercial brands of fermented milks in Thailand were evaluated by consumers (n=71) using hedonic scale for four attributes (sweetness, sourness, flavour, and overall liking), volatile compounds using headspace-solid phase microextraction (HS-SPME) GC-MS, pH, TA, TSS and LAB count. Then the relations were analyzed by principal component analysis (PCA). The PCA data showed that all of four attributes liking scores were related to each other. They were also related to TA, TSS and volatile compounds. The related volatile compounds were mainly on fermented produced compounds including acetic acid, furanmethanol, furfural, octanoic acid and the volatiles known as artificial fruit flavour (beta pinene, limonene, vanillin, and ethyl vanillin). These compounds were provided the information about flavour addition in commercial fermented milk in Thailand.

Keywords: Fermented milk, volatile compounds, preference, PCA.

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Authors: M. F. Miah, H. Ali, E. Zannath, T. M. Shuvra, M. N. Naser, M. K. Ahmed

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In this study, breeding biology and induced breeding of freshwater mud eel, Monopterus cuchia was observed during the experimental period from February to June, 2013. Breeding biology of freshwater mud eel, Monopterus cuchia was considered in terms of gonadosomatic index, length-weight relationship of gonad, ova diameter and fecundity. The ova diameter was recorded from 0.3 mm to 4.30 mm and the individual fecundity was recorded from 155 to 1495 while relative fecundity was found from 2.64 to 12.45. The fecundity related to body weight and length of fish was also discussed. A peak of GSI was observed 2.14±0.2 in male and 5.1 ±1.09 in female. Induced breeding of freshwater mud eel, Monopterus cuchia was also practiced with different doses of different inducing agents like pituitary gland (PG), human chorionic gonadotropin (HCG), Gonadotropin releasing hormone (GnRH) and Ovuline-a synthetic hormone in different environmental conditions. However, it was observed that the artificial breeding of freshwater mud eel, Monopterus cuchia was not yet succeeded through inducing agents in captive conditions, rather the inducing agent showed negative impacts on fecundity and ovarian tissues. It was seen that mature eggs in the oviduct were reduced, absorbed and some eggs were found in spoiled condition.

Keywords: Breeding biology, induced breeding, Monopterus cuchia.

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Authors: Jingming Wu, Ruikang Hu, Junqi Yue, Zhaoguang Yang, Lifeng Zhang

Abstract:

Fecal sterol has been proposed as a chemical indicator of human fecal pollution even when fecal coliform populations have diminished due to water chlorination or toxic effects of industrial effluents. This paper describes an improved derivatization procedure for simultaneous determination of four fecal sterols including coprostanol, epicholestanol, cholesterol and cholestanol using gas chromatography-mass spectrometry (GC-MS), via optimization study on silylation procedures using N-O-bis (trimethylsilyl)-trifluoroacetamide (BSTFA), and N-(tert-butyldimethylsilyl)-N-methyltrifluoroacetamide (MTBSTFA), which lead to the formation of trimethylsilyl (TMS) and tert-butyldimethylsilyl (TBS) derivatives, respectively. Two derivatization processes of injection-port derivatization and water bath derivatization (60 oC, 1h) were inspected and compared. Furthermore, the methylation procedure at 25 oC for 2h with trimethylsilydiazomethane (TMSD) for fecal sterols analysis was also studied. It was found that most of TMS derivatives demonstrated the highest sensitivities, followed by methylated derivatives. For BSTFA or MTBSTFA derivatization processes, the simple injection-port derivatization process could achieve the same efficiency as that in the tedious water bath derivatization procedure.

Keywords: Fecal Sterols, Methylation, Silylation, BSTFA, MTBSTFA, TMSD, GC-MS.

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Authors: Taisir Eldos, Aws Kanan, Waleed Nazih, Ahmad Khatatbih

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Chemical Reaction Optimization (CRO) is an optimization metaheuristic inspired by the nature of chemical reactions as a natural process of transforming the substances from unstable to stable states. Starting with some unstable molecules with excessive energy, a sequence of interactions takes the set to a state of minimum energy. Researchers reported successful application of the algorithm in solving some engineering problems, like the quadratic assignment problem, with superior performance when compared with other optimization algorithms. We adapted this optimization algorithm to the Printed Circuit Board Drilling Problem (PCBDP) towards reducing the drilling time and hence improving the PCB manufacturing throughput. Although the PCBDP can be viewed as instance of the popular Traveling Salesman Problem (TSP), it has some characteristics that would require special attention to the transactions that explore the solution landscape. Experimental test results using the standard CROToolBox are not promising for practically sized problems, while it could find optimal solutions for artificial problems and small benchmarks as a proof of concept.

Keywords: Evolutionary Algorithms, Chemical Reaction Optimization, Traveling Salesman, Board Drilling.

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Authors: Hyun-Woo Cho

Abstract:

Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: Process Monitoring, kernel methods, multivariate filtering, data-driven techniques, quality improvement.

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Authors: Gayadhar Panda, Sidhartha Panda, C. Ardil

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The main objective of Automatic Generation Control (AGC) is to balance the total system generation against system load losses so that the desired frequency and power interchange with neighboring systems is maintained. Any mismatch between generation and demand causes the system frequency to deviate from its nominal value. Thus high frequency deviation may lead to system collapse. This necessitates a very fast and accurate controller to maintain the nominal system frequency. This paper deals with a novel approach of artificial intelligence (AI) technique called Hybrid Neuro-Fuzzy (HNF) approach for an (AGC). The advantage of this controller is that it can handle the non-linearities at the same time it is faster than other conventional controllers. The effectiveness of the proposed controller in increasing the damping of local and inter area modes of oscillation is demonstrated in a two area interconnected power system. The result shows that intelligent controller is having improved dynamic response and at the same time faster than conventional controller.

Keywords: Automatic Generation Control (AGC), Dynamic Model, Two-area Power System, Fuzzy Logic Controller, Neural Network, Hybrid Neuro-Fuzzy(HNF).

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Authors: Norbik Bashah, Idris Bharanidharan Shanmugam, Abdul Manan Ahmed

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Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated includes neural networks and fuzzy logic with network profiling, that uses simple data mining techniques to process the network data. The proposed system is a hybrid system that combines anomaly, misuse and host based detection. Simple Fuzzy rules allow us to construct if-then rules that reflect common ways of describing security attacks. For host based intrusion detection we use neural-networks along with self organizing maps. Suspicious intrusions can be traced back to its original source path and any traffic from that particular source will be redirected back to them in future. Both network traffic and system audit data are used as inputs for both.

Keywords: Intrusion Detection, Network Security, Data mining, Fuzzy Logic.

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Authors: Ali Mirmohammadi, Arash Taheri, Meysam Mohammadi-Amin

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One of the major parts of a jet engine is air intake, which provides proper and required amount of air for the engine to operate. There are several aerodynamic parameters which should be considered in design, such as distortion, pressure recovery, etc. In this research, the effects of lip ice accretion on pitot intake performance are investigated. For ice accretion phenomenon, two supervised multilayer neural networks (ANN) are designed, one for ice shape prediction and another one for ice roughness estimation based on experimental data. The Fourier coefficients of transformed ice shape and parameters include velocity, liquid water content (LWC), median volumetric diameter (MVD), spray time and temperature are used in neural network training. Then, the subsonic intake flow field is simulated numerically using 2D Navier-Stokes equations and Finite Volume approach with Hybrid mesh includes structured and unstructured meshes. The results are obtained in different angles of attack and the variations of intake aerodynamic parameters due to icing phenomenon are discussed. The results show noticeable effects of ice accretion phenomenon on intake behavior.

Keywords: Artificial Neural Network, Ice Accretion, IntakeAerodynamics, Design Parameters, Finite Volume Method.

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Authors: Javier Barrachina, Piedad Garrido, Manuel Fogue, Julio A. Sanguesa, Francisco J. Martinez

Abstract:

It is very common to observe, especially in Computer Science studies that students have difficulties to correctly understand how some mechanisms based on Artificial Intelligence work. In addition, the scope and limitations of most of these mechanisms are usually presented by professors only in a theoretical way, which does not help students to understand them adequately. In this work, we focus on the problems found when teaching Evolutionary Algorithms (EAs), which imitate the principles of natural evolution, as a method to solve parameter optimization problems. Although this kind of algorithms can be very powerful to solve relatively complex problems, students often have difficulties to understand how they work, and how to apply them to solve problems in real cases. In this paper, we present two interactive graphical applications which have been specially designed with the aim of making Evolutionary Algorithms easy to be understood by students. Specifically, we present: (i) TSPS, an application able to solve the ”Traveling Salesman Problem”, and (ii) FotEvol, an application able to reconstruct a given image by using Evolution Strategies. The main objective is that students learn how these techniques can be implemented, and the great possibilities they offer.

Keywords: Education, evolutionary algorithms, evolution strategies, interactive learning applications.

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Authors: André C. Silva, Mariana R. Barros, Elenice M. S. Silva, Douglas. Y. Marinho, Diego F. Lopes, Débora N. Sousa, Raphael S. Tomáz

Abstract:

Modern agriculture requires productivity, efficiency and quality. Therefore, there is need for agricultural limestone implementation that provides adequate amounts of calcium and magnesium carbonates in order to correct soil acidity. During the limestone process, fine particles (with average size under 400#) are generated. These particles do not have economic value in agricultural and metallurgical sectors due their size. When limestone is used for agriculture purposes, these fine particles can be easily transported by wind generated air pollution. Therefore, briquetting, a mineral processing technique, was used to mitigate this problem resulting in an agglomerated product suitable for agriculture use. Briquetting uses compressive pressure to agglomerate fine particles. It can be aided by agglutination agents, allowing adjustments in shape, size and mechanical parameters of the mass. Briquettes can generate extra profits for mineral industry, presenting as a distinct product for agriculture, and can reduce the environmental liabilities of the fine particles storage or disposition. The produced limestone briquettes were subjected to shatter and water action resistance tests. The results show that after six minutes completely submerged in water, the briquettes where fully diluted, a highly favorable result considering its use for soil acidity correction.

Keywords: Agglomeration, briquetting, limestone, agriculture.

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Authors: T. Rashid, T. Kechadi

Abstract:

This paper is a continuation of our daily energy peak load forecasting approach using our modified network which is part of the recurrent networks family and is called feed forward and feed back multi context artificial neural network (FFFB-MCANN). The inputs to the network were exogenous variables such as the previous and current change in the weather components, the previous and current status of the day and endogenous variables such as the past change in the loads. Endogenous variable such as the current change in the loads were used on the network output. Experiment shows that using endogenous and exogenous variables as inputs to the FFFBMCANN rather than either exogenous or endogenous variables as inputs to the same network produces better results. Experiments show that using the change in variables such as weather components and the change in the past load as inputs to the FFFB-MCANN rather than the absolute values for the weather components and past load as inputs to the same network has a dramatic impact and produce better accuracy.

Keywords: Daily peak load forecasting, feed forward and feedback multi-context neural network.

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